TY - JOUR
T1 - Respiratory syncytial virus hospitalisation by chronological month of age and by birth month in infants
AU - Respiratory Virus Global Epidemiology Network
AU - PROMISE Investigators
AU - Guo, Ling
AU - Kenmoe, Sebastien
AU - Miyake, Fuyu
AU - Chung, Alexandria
AU - Zhang, Han
AU - Bandeira, Teresa
AU - Caballero, Mauricio T.
AU - Casalegno, Jean Sébastien
AU - Fasce, Rodrigo
AU - Giorgi, Chakhunashvili
AU - Heikkinen, Terho
AU - Huang, Q. Sue
AU - Katama, Esther Nyadzua
AU - Keck, James W.
AU - Liu, Enmei
AU - Markic, Josko
AU - Moore, Hannah C.
AU - Moyes, Jocelyn
AU - Rath, Barbara A.
AU - Romero, Candice
AU - Wang, Qianli
AU - Werner, Marta
AU - Yung, Chee Fu
AU - Hernandez, Roger
AU - Bazan, Isabel
AU - Soto, Giselle
AU - Scobie, Heather
AU - Bressler, Sara
AU - Desnoyers, Christine
AU - Cepeda, Alfonso
AU - Bugueño, Andres
AU - Lopez, Olga
AU - Come, Horvat
AU - Ploin, Dominique
AU - Rodriguez, Ivan
AU - Enriquez, Paula
AU - Obermeier, Patrick E.
AU - Walaza, Sibongile
AU - Cohen, Cheryl
AU - Wolter, Nicole
AU - Le, Huong
AU - Taye, Belaynew
AU - Sarna, Mohinder
AU - Mrcela, Dina
AU - Deng, Yu
AU - Zang, Na
AU - Ren, Luo
AU - Kea, Agustus
AU - Mutunga, Martin
AU - Bont, Louis
N1 - © 2025. The Author(s).
PY - 2025/7/3
Y1 - 2025/7/3
N2 - Understanding the distribution of respiratory syncytial virus (RSV) disease burden by more granular age bands in infants is necessary for optimising infant RSV immunisation strategies. Using a Bayesian model, we synthesised published data from a systematic literature review and unpublished data shared by international collaborators for estimating the distribution of infant RSV hospitalisations by month of age. Based on local RSV seasonality data, we further developed and validated a web-based prediction tool for estimating infant RSV hospitalisation distribution by birth month. Although RSV hospitalisation burden mostly peaked at the second month of life and was concentrated in infants under six months globally, substantial variations were noted in the age distribution of RSV hospitalisation among infants born in different months. Passive immunisation strategies should ideally be tailored to the local RSV disease burden distribution by age and birth month to maximise their per-dose effectiveness before a universal immunisation can be achieved.
AB - Understanding the distribution of respiratory syncytial virus (RSV) disease burden by more granular age bands in infants is necessary for optimising infant RSV immunisation strategies. Using a Bayesian model, we synthesised published data from a systematic literature review and unpublished data shared by international collaborators for estimating the distribution of infant RSV hospitalisations by month of age. Based on local RSV seasonality data, we further developed and validated a web-based prediction tool for estimating infant RSV hospitalisation distribution by birth month. Although RSV hospitalisation burden mostly peaked at the second month of life and was concentrated in infants under six months globally, substantial variations were noted in the age distribution of RSV hospitalisation among infants born in different months. Passive immunisation strategies should ideally be tailored to the local RSV disease burden distribution by age and birth month to maximise their per-dose effectiveness before a universal immunisation can be achieved.
KW - Respiratory Syncytial Virus Infections/epidemiology
KW - Age Factors
KW - Humans
KW - Bayes Theorem
KW - Respiratory Syncytial Virus, Human
KW - Hospitalization/statistics & numerical data
KW - Infant
KW - Seasons
KW - Infant, Newborn
UR - https://www.scopus.com/pages/publications/105010547537
UR - https://www.mendeley.com/catalogue/b8c6b19d-ad4c-3e34-8b21-551c6d4c425c/
U2 - 10.1038/s41467-025-61400-1
DO - 10.1038/s41467-025-61400-1
M3 - Article
C2 - 40610449
AN - SCOPUS:105010547537
SN - 2041-1723
VL - 16
JO - Nature communications
JF - Nature communications
IS - 1
M1 - 6109
ER -